class_docstr: Derivative Algorithm.
  This method is the derivative version of Method 1.
  Instead of data point value, it uses the derivative of the data point.
docstr: Initializer
  :param TimeSeries time_series: a TimeSeries object.
  :param TimeSeries baseline_time_series: baseline TimeSeries.
  :param float smoothing_factor: smoothing factor.
docstr_tok: ["Initializer",":","param","TimeSeries","time_series",":","a","TimeSeries","object",".",":","param","TimeSeries","baseline_time_series",":","baseline","TimeSeries",".",":","param","float","smoothing_factor",":","smoothing","factor","."]
code: def __init__(
      self,
      time_series,
      baseline_time_series=None,
      smoothing_factor=None,
  ):
      """
      Initializer
      :param TimeSeries time_series: a TimeSeries object.
      :param TimeSeries baseline_time_series: baseline TimeSeries.
      :param float smoothing_factor: smoothing factor.
      """
      super(DerivativeDetector, self).__init__(
          self.__class__.__name__, time_series, baseline_time_series
      )
      self.smoothing_factor = (
          smoothing_factor or DEFAULT_DERI_SMOOTHING_FACTOR
      )
      self.time_series_items = self.time_series.items()
  
file_path: luminol/src/luminol/algorithms/anomaly_detector_algorithms/derivative_detector.py
imports: import numpy
  from luminol import utils
  from luminol.algorithms.anomaly_detector_algorithms import AnomalyDetectorAlgorithm
  from luminol.constants import DEFAULT_DERI_SMOOTHING_FACTOR
  from luminol.modules.time_series import TimeSeries
label_desc: Use image node_id 1 to create a new DerivativeDetector object from inherited base classes: AnomalyDetectorAlgorithm with example: obj = DerivativeDetector(time_series, baseline_time_series, smoothing_factor)